3 citations · 3 across the 4 of their papers we have counts for
4 papers
CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning
Kexin Bao, Daichi Zhang, Hansong Zhang +3
Few-shot class-incremental learning (FSCIL) receives significant attention from the public to perform classification continuously with a few training samples, which suffers from th…
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Kexin Baoa, Fanzhao Lin, Zichen Wang +3
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting a…
Few-shot Class-Incremental Learning via Generative Co-Memory Regularization
Kexin Bao, Yong Li, Dan Zeng +1
Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of mod…
Federated Learning with Label-Masking Distillation
Jianghu Lu, Shikun Li, Kexin Bao +3
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In…